click.sim {ClickClust}

Simulating sequences of visited states

Description

Simulates sequences of visited states.

Usage

click.sim(n, int = c(5, 100), alpha, beta = NULL, gamma)

Arguments

n

number of sequences

int

interval defining the lower and upper bounds for the length of sequences

alpha

vector of mixing proportions (length K)

beta

matrix of initial state probabilities (K x p)

gamma

array of K p x p transition probability matrices (p x p x K)

Details

Simulates 'n' sequences of visited states according to the following mixture model parameters: 'alpha' - mixing proportions, 'beta' - initial state probabilities, 'gamma' - transition probability matrices. If the matrix 'beta' is not provided, all initial states are assumed to be equal to 1 / p.

Value

S

list of simulated sequences

id

true classification of simulated sequences

Author(s)

Melnykov, V.

References

Melnykov, V. (2016) Model-Based Biclustering of Clickstream Data, Computational Statistics and Data Analysis, 93, 31-45.

Melnykov, V. (2016) ClickClust: An R Package for Model-Based Clustering of Categorical Sequences, Journal of Statistical Software, 74, 1-34.

See Also

click.read, click.EM

Examples

# SPECIFY MODEL PARAMETERS

set.seed(123)

n.seq <- 20

p <- 5
K <- 2
mix.prop <- c(0.3, 0.7)


TP1 <- matrix(c(0.20, 0.10, 0.15, 0.15, 0.40,
                0.20, 0.20, 0.20, 0.20, 0.20,
                0.15, 0.10, 0.20, 0.20, 0.35,
                0.15, 0.10, 0.20, 0.20, 0.35,
                0.30, 0.30, 0.10, 0.10, 0.20), byrow = TRUE, ncol = p)

TP2 <- matrix(c(0.15, 0.15, 0.20, 0.20, 0.30,
                0.20, 0.10, 0.30, 0.30, 0.10,
                0.25, 0.20, 0.15, 0.15, 0.25,
                0.25, 0.20, 0.15, 0.15, 0.25,
                0.10, 0.30, 0.20, 0.20, 0.20), byrow = TRUE, ncol = p)


TP <- array(rep(NA, p * p * K), c(p, p, K))
TP[,,1] <- TP1
TP[,,2] <- TP2


# DATA SIMULATION

A <- click.sim(n = n.seq, int = c(10, 50), alpha = mix.prop, gamma = TP)
A